Mortality trends in type 1 diabetes: a multicountry analysis of six population-based cohorts

Aims/hypothesis Mortality has declined in people with type 1 diabetes in recent decades. We examined how the pattern of decline differs by country, age and sex, and how mortality trends in type 1 diabetes relate to trends in general population mortality. Methods We assembled aggregate data on all-cause mortality during the period 2000–2016 in people with type 1 diabetes aged 0–79 years from Australia, Denmark, Latvia, Scotland, Spain (Catalonia) and the USA (Kaiser Permanente Northwest). Data were obtained from administrative sources, health insurance records and registries. All-cause mortality rates in people with type 1 diabetes, and standardised mortality ratios (SMRs) comparing type 1 diabetes with the non-diabetic population, were modelled using Poisson regression, with age and calendar time as quantitative variables, describing the effects using restricted cubic splines with six knots for age and calendar time. Mortality rates were standardised to the age distribution of the aggregate population with type 1 diabetes. Results All six data sources showed a decline in age- and sex-standardised all-cause mortality rates in people with type 1 diabetes from 2000 to 2016 (or a subset thereof), with annual changes in mortality rates ranging from −2.1% (95% CI −2.8%, −1.3%) to −5.8% (95% CI −6.5%, −5.1%). All-cause mortality was higher for male individuals and for older individuals, but the rate of decline in mortality was generally unaffected by sex or age. SMR was higher in female individuals than male individuals, and appeared to peak at ages 40–70 years. SMR declined over time in Denmark, Scotland and Spain, while remaining stable in the other three data sources. Conclusions/interpretation All-cause mortality in people with type 1 diabetes has declined in recent years in most included populations, but improvements in mortality relative to the non-diabetic population are less consistent. Graphical abstract Electronic supplementary material The online version of this article (10.1007/s00125-022-05659-9) contains peer-reviewed but unedited supplementary material, which is available to authorised users.


Modified Newcastle-Ottawa Quality Assessment Scale for mortality trends in type 1 diabetes.
A study can be awarded a maximum of one or two points for each numbered item within each category.  . In addition, registrants who were originally classified as type 2 on the NDSS, but whose age at diagnosis was <30 years and time to insulin was <1 year, were reclassified as type 1 diabetes. Denmark Algorithm incorporating clinical diagnosis (ICD codes) from the hospitalisations or outpatient clinics, prescription of anti-diabetic medications, clinical and billing records.
Persons were classified as having type 1 diabetes in the diabetes register if any of the following criteria were met, and otherwise as type 2 diabetes: 1) Purchase of insulin before age 30, or 2) DADD: classified as having type 1 diabetes in >50% of the person's DADD records classify the person as type 1 diabetes, and similarly for type 2 diabetes, or 3) Not classified as either type 1 or type 2 diabetes in DADD, but >50% of the patient's records from National Patient Register classifies the person as having type 1 diabetes. Finally, a person cannot be classified as having type 1 diabetes if there is no recorded date of insulin purchase [2].

Diagnostic method
Classification of diabetes type Latvia Clinical diagnosis using ICD-10 codes. Persons were classified as having type 1 diabetes according to ICD-10 codes.

Scotland
Clinical diagnosis using the Read code system.
Persons were classified as having type 1 diabetes using clinician-assigned diabetes type for each individual, which was accepted unless contradictory prescription history or age-at-diagnosis data were available (e.g. a clinician-assigned diagnosis of type 1 diabetes without any subsequent insulin prescription would not be accepted) [3].
Persons were classified as having type 1 diabetes according to ICD-10 codes.

USA (KPNW)
Algorithm incorporating hospitalisation with diabetes as primary discharge diagnosis (ICD codes), ≥2 outpatient visits (ICD codes), anti-diabetic medications or two abnormal blood results from an integrated healthcare delivery system.
Persons were classified as having type 1 diabetes if >50% of the person's records had a diagnosis code of type 1 diabetes within a 2-year period [4].  12.0 7.9 (5.6, 11.2) Mortality rates are standardised to the age and sex distribution of the assembled population with type 1 diabetes within the six data sources, with equal weights for male and female individuals.  ESM Fig. 1 Crude all-cause mortality rates in people with type 1 diabetes stratified by sex.

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Male individuals in full lines, female individuals in broken lines. The y-axis is plotted on a natural logarithmic scale. Fig. 2 Age-standardised all-cause mortality rates in people with type 1 diabetes stratified by sex.

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Male individuals in full lines, female individuals in broken lines. Standardisation is based on annual age-specific mortality rates from age-period-cohort models fitted separately for each data source and sex. Standard population was derived from the pooled study population with type 1 diabetes within the six data sources, with equal weights for male and female individuals. Shaded areas represent 95% CI around mortality trends. The y-axis is plotted on a natural logarithmic scale. Fig. 4 Estimated mortality rates in people with type 1 diabetes by age and period in Denmark.

ESM
Male individuals in blue (left panels), female individuals in red (right panels). Estimates of mortality rates are from age-period-cohort models, fitted separately for male and female individuals. Upper panels show age-specific rates at different dates, as indicated by vertical lines in the lower panels. Lower panels show period-specific rates at different ages, as indicated by vertical lines in the upper panels. The y-axis is plotted on a natural logarithmic scale. Fig. 5 Estimated mortality rates in people with type 1 diabetes by age and period in Latvia.

ESM
Male individuals in blue (left panels), female individuals in red (right panels). Estimates of mortality rates are from age-period-cohort models, fitted separately for male and female individuals. Upper panels show age-specific rates at different dates, as indicated by vertical lines in the lower panels. Lower panels show period-specific rates at different ages, as indicated by vertical lines in the upper panels. The y-axis is plotted on a natural logarithmic scale. Fig. 6 Estimated mortality rates in people with type 1 diabetes by age and period in Scotland.

ESM
Male individuals in blue (left panels), female individuals in red (right panels). Estimates of mortality rates are from age-period-cohort models, fitted separately for male and female individuals. Upper panels show age-specific rates at different dates, as indicated by vertical lines in the lower panels. Lower panels show period-specific rates at different ages, as indicated by vertical lines in the upper panels. The y-axis is plotted on a natural logarithmic scale. Fig. 7 Estimated mortality rates in people with type 1 diabetes by age and period in Spain.

ESM
Male individuals in blue (left panels), female individuals in red (right panels). Estimates of mortality rates are from age-period-cohort models, fitted separately for male and female individuals. Upper panels show age-specific rates at different dates, as indicated by vertical lines in the lower panels. Lower panels show period-specific rates at different ages, as indicated by vertical lines in the upper panels. The y-axis is plotted on a natural logarithmic scale. Fig. 8 Estimated mortality rates in people with type 1 diabetes by age and period in the USA (KPNW).

ESM
Male individuals in blue (left panels), female individuals in red (right panels). Estimates of mortality rates are from age-period-cohort models, fitted separately for male and female individuals. Upper panels show age-specific rates at different dates, as indicated by vertical lines in the lower panels. Lower panels show period-specific rates at different ages, as indicated by vertical lines in the upper panels. The y-axis is plotted on a natural logarithmic scale. ESM Fig. 9 Age by time interaction of mortality rates as estimated annual change in mortality by age.
Estimated annual change in mortality was computed from a model with calendar time trend varying by age, either by smooth splines or linearly; both plotted in each panel. The curves were derived using a spline for age (a varying coefficients model), while the straight lines using the product of age and calendar time.